Stochastic modelling provides an indispensable tool for understanding how random events at the molecular level influence cellular functions. In practice, the common challenge is to calibrate a large number of model parameters against the experimental data. A related problem is to efficiently study how the behaviour of a stochastic model depends on its parameters, i.e. whether a change in model parameters can lead to a significant qualitative change in model behaviour (bifurcation). In this paper, tensor-structured parametric analysis (TPA) is presented. It is based on recently proposed low-parametric tensor-structured representations of classical matrices and vectors. This approach enables simultaneous computation of the model properties for all parameter values within a parameter space. This methodology is exemplified to study the parameter estimation, robustness, sensitivity and bifurcation structure in stochastic models of biochemical networks. The TPA has been implemented in Matlab and the codes are available at http://www.stobifan.org .
Effect of cell heterogeneity on isogenic populations with the synthetic genetic toggle switch network: bifurcation analysis of two-dimensional Cell Population Balance Models
Stochastic gene regulatory networks with bursting dynamics can be modeled mesocopically as a generalized density-dependent Markov chain (GDDMC) or macroscopically as a piecewise-deterministic Markov process (PDMP). Here we prove a limit theorem showing that each family of GDDMCs will converge to a PDMP as the system size tends to infinity. Moreover, under a simple dissipative condition, we prove the existence and uniqueness of the stationary distribution and the exponential ergodicity for the PDMP limit via the coupling method. Further extensions and applications to single-cell stochastic gene…
Identification of genes that initiate cell anomalies and cause cancer in humans is among the important fields in the oncology researches. The mutation and development of anomalies in these genes are then transferred to other genes in the cell and therefore disrupt the normal functionality of the cell. These genes are known as cancer driver genes (CDGs). Various methods have been proposed for predicting CDGs, most of which based on genomic data and based on computational methods. Therefore, some researchers have developed novel bioinformatics approaches. In this study, we propose an algorithm, …
Developing and maintaining life requires a lot of computation. This is done by gene regulatory networks. But we have little understanding of how this computation is organized. I show that there is a direct correspondence between the structural and functional building blocks of regulatory networks, which I call regulatory motifs. I derive a simple bound on the range of function that these motifs can perform, in terms of the local network structure. I prove that this range is a small fraction of all possible functions, which severely constrains global network behavior. Part of this restriction i…
The biological processes of cellular decision making and differentiation involve a plethora of signalling pathways and gene regulatory circuits. These networks, in their turn, exhibit a multitude of motifs playing crucial parts in regulating network activity. Here, we compare the topological placement of motifs in gene regulatory and signalling networks and find that it suggests different evolutionary strategies in motif distribution for distinct cellular subnetworks.
Gene regulatory networks are powerful abstractions of biological systems. Since the advent of high-throughput measurement technologies in biology in the late 90s, reconstructing the structure of such networks has been a central computational problem in systems biology. While the problem is certainly not solved in its entirety, considerable progress has been made in the last two decades, with mature tools now available. This chapter aims to provide an introduction to the basic concepts underpinning network inference tools, attempting a categorisation which highlights commonalities and relative …
Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the always increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorit…
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
We present a Bayesian hierarchical multi-view mixture model termed Symphony that simultaneously learns clusters of cells representing cell types and their underlying gene regulatory networks by integrating data from two views: single-cell gene expression data and paired epigenetic data, which is informative of gene-gene interactions. This model improves interpretation of clusters as cell types with similar expression patterns as well as regulatory networks driving expression, by explaining gene-gene covariances with the biological machinery regulating gene expression. We show the theoretical a…
Cancer disease occurs because of a disorder in the cellular regulatory mechanism, Which causes cellular malformation. The genes that start the malformation are called Cancer driver genes (CDGs) . Numerous computational methods have been introduced to identify cancer driver genes that use the concept of mutation.Regarding abnormalities spread in human cell and tumor development, CDGs are likely to be the potential types of gene with high influence in the network. This increases the importance of influence diffusion concept for the identification of CDGs.recently developed a method based on infl…
Background:Gene regulations often change over time rather than being constant. But many of gene regulatory networks extracted from databases are static. The tumor suppressor gene $P53$ is involved in the pathogenesis of many tumors, and its inhibition effects occur after a certain period. Therefore, it is of great significance to elucidate the regulation mechanism over time points. Result:A qualitative method for representing dynamic gene regulatory network is developed, called CdtGRN. It adopts the combination of convolutional neural networks(CNN) and fully connected networks(DNN) as the core…
Genes are frequently regulated in complex manners, necessitating modelling approaches which go beyond simple (linear) gene-to-gene interactions and address the modularity of cis-regulatory regions and alternate transcription initiation sites. In particular, sharp expression patterns (peaks or stripes) indicate that gene regulation involves nonlinear transcription factor kinetics (beyond first-order). We propose a methodology for approaching this problem, using the example of the multiple cis-regulatory modules (CRMs) and two transcripts (P1 and P2) found in the Drosophila hunchback (hb) and (C…
Generalization of the construction method for multistability-equivalent gene regulatory networks to systems with multi-input multi-output loopbreaking
Schittler et al.（2013）最近解决了不同维度的基因调控网络模型之间多稳定性方面的等价性问题。作者在这项工作中提出了一个高维动力系统的建设规则，当给定一个低维动力系统和高维网络结构。然而，其中的证明仅限于所有内部反馈回路都可以通过产生单输入单输出（SISO）系统的回路破坏方法来破坏的系统类。在这份报告中，我们提出了推广的证明，任何数量的内部反馈回路，这将被打破了一个广义的破环的方法，从而在一个多输入多输出（MIMO）系统的系统。这种方法的推广使得构建方法适用于广泛的一类基因调控网络模型，从而促进了从核心基序模型到更现实的高维基因调控模型的结果转移。我们通过将其应用于间充质干细胞分化中的基因调控网络的一个例子来证明我们的方法的潜力和价值。
Biological systems and processes are networks of complex nonlinear regulatory interactions between nucleic acids, proteins, and metabolites. A natural way in which to represent these interaction networks is through the use of a graph. In this formulation, each node represents a nucleic acid, protein, or metabolite and edges represent intermolecular interactions (inhibition, regulation, promotion, coexpression, etc.). In this work, a novel algorithm for the discovery of latent graph structures given experimental data is presented.
University of Cambridge（剑桥大学）；Computer Laboratory（计算机实验室）
Delays in protein synthesis cause a confounding effect when constructing Gene Regulatory Networks (GRNs) from RNA-sequencing time-series data. Accurate GRNs can be very insightful when modelling development, disease pathways, and drug side-effects. We present a model which incorporates translation delays by combining mechanistic equations and Bayesian approaches to fit to experimental data. This enables greater biological interpretability, and the use of Gaussian processes enables non-linear expressivity through kernels as well as naturally accounting for biological variation.
SIREN Cytoscape plugin: Interaction Type Discrimination in Gene Regulatory Networks
将表达数据与网络中的基因相互作用相结合对于理解细胞的功能组织至关重要。因此，在生物网络中的相互作用类型的知识是重要的数据解释。Signing of Regulatory Networks（SIREN）plugin for Cytoscape是一个开源Java工具，用于区分基因调控网络中的相互作用类型（激活或抑制）。SIREN利用基于信息论的概念，试图通过检查它们相应的基因表达谱来识别基因对的相互作用类型。我们介绍SIREN，一个快速和内存高效的工具，具有低计算复杂度，使用户可以很容易地将其视为许多网络重建方法的补充方法。SIREN允许生物学家使用独立的表达数据来预测已知基因调控网络的相互作用类型，其中重建方法不提供关于其相互作用类型的性质的任何信息。SIREN Cytoscape插件是用Java实现的，可以在http://baderlab.org/Software/SIRENplugin和Cytoscape应用程序管理器上免费获得。
Regulatory networks describe the interactions between molecular or cellular regulators, like transcription factors and genes in gene regulatory networks, kinases and their receptors in signalling networks, or neurons in neural networks. A long-standing aim of quantitative biology is to reconstruct such networks on the basis of large-scale data. Our aim is to leverage fluctuations around the non-equilibrium steady state for network inference. To this end, we use a stochastic model of gene regulation or neural dynamics and solve it approximately within a Gaussian mean-field theory. We develop a …
Modeling Dynamics, Cell Type Specificity, and Perturbations in Gene Regulatory Networks
Wisconsin Institute for Discovery, University of Wisconsin-Madison（威斯康星发现研究所，威斯康星大学麦迪逊分校）；Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison（威斯康星大学麦迪逊分校生物统计与医学信息学系）
In this paper, we conduct theoretical analyses on inferring the structure of gene regulatory networks. Depending on the experimental method and data type, the inference problem is classified into 20 different scenarios. For each scenario, we discuss the problem that with enough data, under what assumptions, what can be inferred about the structure. For scenarios that have been covered in the literature, we provide a brief review. For scenarios that have not been covered in literature, if the structure can be inferred, we propose new mathematical inference methods and evaluate them on simulated…
